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Hasselgren C, Kenyon M, Anger LT, Cornwell P, Watt E, Bercu J. Analysis of non-mutagenic substances in the context of drug impurity assessment - Few are potent toxicants. Regul Toxicol Pharmacol 2024; 150:105645. [PMID: 38761967 DOI: 10.1016/j.yrtph.2024.105645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Revised: 05/07/2024] [Accepted: 05/13/2024] [Indexed: 05/20/2024]
Abstract
ICH Q3A/B guidelines provide qualification thresholds for impurities or degradation products in new drug substances and products. However, the guidelines note that certain impurities/degradation products may warrant further safety evaluation for being unusually potent or toxic. The purpose of this study was to confirm that especially toxic non-mutagenic compounds are rare and to identify classes of compounds that could warrant lower qualification thresholds. A total of 2815 compounds were evaluated, of which 2213 were assessed as non-mutagenic. For the purpose of this analysis, compounds were considered potent when the point of departure was ≤0.2 mg/kg/day based on the qualification threshold (1 mg/day or 0.02 mg/kg/day for a 50 kg human) in a new drug substance, with an additional 10-fold margin. Only 54 of the entire set (2.4%) would be considered potent based on this conservative potency analysis, confirming that the existing ICH Q3A/B qualification thresholds are appropriate for the majority of impurities. If the Q3A/B threshold, without the additional 10-fold margin is used, 14 compounds (0.6%) are considered "highly potent". Very few non-mutagenic structural classes were identified, including organothiophosphates and derivatives, polychlorinated benzenes and polychlorinated polycyclic aliphatics, that correlate with potential high potency, consistent with prior publications.
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Affiliation(s)
- Catrin Hasselgren
- Department of Safety Assessment, Genentech, Inc., South San Francisco, CA, 94080, USA.
| | - Michelle Kenyon
- Drug Safety Research and Development, Pfizer Research and Development, Groton, CT, 06340, USA
| | - Lennart T Anger
- Department of Safety Assessment, Genentech, Inc., South San Francisco, CA, 94080, USA
| | - Paul Cornwell
- Nonclinical Safety Assessment, Eli Lilly & Co, Indianapolis, IN, 46285, USA
| | - Eric Watt
- Drug Safety Research and Development, Pfizer Research and Development, Groton, CT, 06340, USA
| | - Joel Bercu
- Gilead Sciences, Inc., Nonclinical Safety and Pathobiology (NSP), Foster City, CA, 94404, USA
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2
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Bishop PL, Mansouri K, Eckel WP, Lowit MB, Allen D, Blankinship A, Lowit AB, Harwood DE, Johnson T, Kleinstreuer NC. Evaluation of in silico model predictions for mammalian acute oral toxicity and regulatory application in pesticide hazard and risk assessment. Regul Toxicol Pharmacol 2024; 149:105614. [PMID: 38574841 DOI: 10.1016/j.yrtph.2024.105614] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 01/29/2024] [Accepted: 03/27/2024] [Indexed: 04/06/2024]
Abstract
The United States Environmental Protection Agency (USEPA) uses the lethal dose 50% (LD50) value from in vivo rat acute oral toxicity studies for pesticide product label precautionary statements and environmental risk assessment (RA). The Collaborative Acute Toxicity Modeling Suite (CATMoS) is a quantitative structure-activity relationship (QSAR)-based in silico approach to predict rat acute oral toxicity that has the potential to reduce animal use when registering a new pesticide technical grade active ingredient (TGAI). This analysis compared LD50 values predicted by CATMoS to empirical values from in vivo studies for the TGAIs of 177 conventional pesticides. The accuracy and reliability of the model predictions were assessed relative to the empirical data in terms of USEPA acute oral toxicity categories and discrete LD50 values for each chemical. CATMoS was most reliable at placing pesticide TGAIs in acute toxicity categories III (>500-5000 mg/kg) and IV (>5000 mg/kg), with 88% categorical concordance for 165 chemicals with empirical in vivo LD50 values ≥ 500 mg/kg. When considering an LD50 for RA, CATMoS predictions of 2000 mg/kg and higher were found to agree with empirical values from limit tests (i.e., single, high-dose tests) or definitive results over 2000 mg/kg with few exceptions.
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Affiliation(s)
- Patricia L Bishop
- Animal Research Issues, The Humane Society of the United States, Washington, DC, USA.
| | - Kamel Mansouri
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, National Institute of Environmental Health Sciences, Research Triangle Park, NC, USA
| | - William P Eckel
- US Environmental Protection Agency, Office of Pesticide Programs, Washington, DC, USA
| | - Michael B Lowit
- US Environmental Protection Agency, Office of Pesticide Programs, Washington, DC, USA
| | - David Allen
- Predictive Toxicology and Information Sciences Group, Discovery and Safety Assessment Division, Inotiv, Morrisville, NC, USA
| | - Amy Blankinship
- US Environmental Protection Agency, Office of Pesticide Programs, Washington, DC, USA
| | - Anna B Lowit
- US Environmental Protection Agency, Office of Pollution Prevention and Toxics, Washington, DC, USA
| | - D Ethan Harwood
- US Environmental Protection Agency, Office of Pesticide Programs, Washington, DC, USA
| | - Tamara Johnson
- US Environmental Protection Agency, Office of Pesticide Programs, Washington, DC, USA
| | - Nicole C Kleinstreuer
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, National Institute of Environmental Health Sciences, Research Triangle Park, NC, USA
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3
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Hasselgren C, Oprea TI. Artificial Intelligence for Drug Discovery: Are We There Yet? Annu Rev Pharmacol Toxicol 2024; 64:527-550. [PMID: 37738505 DOI: 10.1146/annurev-pharmtox-040323-040828] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/24/2023]
Abstract
Drug discovery is adapting to novel technologies such as data science, informatics, and artificial intelligence (AI) to accelerate effective treatment development while reducing costs and animal experiments. AI is transforming drug discovery, as indicated by increasing interest from investors, industrial and academic scientists, and legislators. Successful drug discovery requires optimizing properties related to pharmacodynamics, pharmacokinetics, and clinical outcomes. This review discusses the use of AI in the three pillars of drug discovery: diseases, targets, and therapeutic modalities, with a focus on small-molecule drugs. AI technologies, such as generative chemistry, machine learning, and multiproperty optimization, have enabled several compounds to enter clinical trials. The scientific community must carefully vet known information to address the reproducibility crisis. The full potential of AI in drug discovery can only be realized with sufficient ground truth and appropriate human intervention at later pipeline stages.
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Affiliation(s)
- Catrin Hasselgren
- Safety Assessment, Genentech, Inc., South San Francisco, California, USA
| | - Tudor I Oprea
- Expert Systems Inc., San Diego, California, USA;
- Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, New Mexico, USA
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Moudgal C, Anger LT, Muster W, Nguyen R, Melnikov F, Siramshetty VB, Graham J. The application of acute oral toxicity computational models in dangerous goods classification. Toxicol Ind Health 2023; 39:687-699. [PMID: 37860984 DOI: 10.1177/07482337231209091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2023]
Abstract
Acute oral toxicity (AOT) data inform the acute toxicity potential of a compound and guides occupational safety and transportation practices. AOT data enable the categorization of a chemical into the appropriate AOT Globally Harmonized System (GHS) category based on the severity of the hazard. AOT data are also utilized to identify compounds that are Dangerous Goods (DGs) and subsequent transportation guidance for shipping of these hazardous materials. Proper identification of DGs is challenging for novel compounds that lack data. It is not feasible to err on the side of caution for all compounds lacking AOT data and to designate them as DGs, as shipping a compound as a DG has cost, resource, and time implications. With the wealth of available historical AOT data, AOT testing approaches are evolving, and in silico AOT models are emerging as tools that can be utilized with confidence to assess the acute toxicity potential of de novo molecules. Such approaches align with the 3R principles, offering a reduction or even replacement of traditional in vivo testing methods and can also be leveraged for product stewardship purposes. Utilizing proprietary historical in vivo AOT data for 210 pharmaceutical compounds (PCs), we evaluated the performance of two established in silico AOT programs: the Leadscope AOT Model Suite and the Collaborative Acute Toxicity Modeling Suite. These models accurately identified 94% and 97% compounds that were not DGs (GHS categories 4, 5, and not classified (NC)) suggesting that the models are fit-for-purpose in identifying PCs with low acute oral toxicity potential (LD50 >300 mg/kg). Utilization of these models to identify compounds that are not DGs can enable them to be de-prioritized for in vivo testing. This manuscript provides a detailed evaluation and assessment of the two models and recommends the most suitable applications of such models.
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Affiliation(s)
| | - Lennart T Anger
- Safety Assessment, Genentech Inc, South San Francisco, CA, USA
| | | | - Ruthi Nguyen
- EHS, Genentech Inc, South San Francisco, CA, USA
| | - Fjodor Melnikov
- Safety Assessment, Genentech Inc, South San Francisco, CA, USA
| | | | - Jessica Graham
- Safety Assessment, Genentech Inc, South San Francisco, CA, USA
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Zwickl CM, Graham J, Jolly R, Bassan A, Ahlberg E, Amberg A, Anger LT, Barton-Maclaren T, Beilke L, Bellion P, Brigo A, Cronin MT, Custer L, Devlin A, Burleigh-Flayers H, Fish T, Glover K, Glowienke S, Gromek K, Jones D, Karmaus A, Kemper R, Piparo EL, Madia F, Martin M, Masuda-Herrera M, McAtee B, Mestre J, Milchak L, Moudgal C, Mumtaz M, Muster W, Neilson L, Patlewicz G, Paulino A, Roncaglioni A, Ruiz P, Suarez D, Szabo DT, Valentin JP, Vardakou I, Woolley D, Myatt G. Principles and Procedures for Assessment of Acute Toxicity Incorporating In Silico Methods. COMPUTATIONAL TOXICOLOGY (AMSTERDAM, NETHERLANDS) 2022; 24:100237. [PMID: 36818760 PMCID: PMC9934006 DOI: 10.1016/j.comtox.2022.100237] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Acute toxicity in silico models are being used to support an increasing number of application areas including (1) product research and development, (2) product approval and registration as well as (3) the transport, storage and handling of chemicals. The adoption of such models is being hindered, in part, because of a lack of guidance describing how to perform and document an in silico analysis. To address this issue, a framework for an acute toxicity hazard assessment is proposed. This framework combines results from different sources including in silico methods and in vitro or in vivo experiments. In silico methods that can assist the prediction of in vivo outcomes (i.e., LD50) are analyzed concluding that predictions obtained using in silico approaches are now well-suited for reliably supporting assessment of LD50-based acute toxicity for the purpose of GHS classification. A general overview is provided of the endpoints from in vitro studies commonly evaluated for predicting acute toxicity (e.g., cytotoxicity/cytolethality as well as assays targeting specific mechanisms). The increased understanding of pathways and key triggering mechanisms underlying toxicity and the increased availability of in vitro data allow for a shift away from assessments solely based on endpoints such as LD50, to mechanism-based endpoints that can be accurately assessed in vitro or by using in silico prediction models. This paper also highlights the importance of an expert review of all available information using weight-of-evidence considerations and illustrates, using a series of diverse practical use cases, how in silico approaches support the assessment of acute toxicity.
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Affiliation(s)
| | - Jessica Graham
- Genentech, Inc., 1 DNA Way, South San Francisco, CA 94080, USA
| | - Robert Jolly
- Eli Lilly and Company, Indianapolis, IN 46285, USA
| | - Arianna Bassan
- Innovatune srl, Via Giulio Zanon 130/D, 35129 Padova, Italy
| | - Ernst Ahlberg
- Universal Prediction AB, Gothenburg, Sweden
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | - Alexander Amberg
- Sanofi, R&D Preclinical Safety Frankfurt, Industriepark Hoechst, D-65926 Frankfurt am Main, Germany
| | | | - Tara Barton-Maclaren
- Healthy Environments and Consumer Safety Branch, Health Canada / Government of Canada
| | - Lisa Beilke
- Toxicology Solutions, Inc., 10531 4S Commons Dr. #594, San Diego, CA 92127, USA
| | - Phillip Bellion
- Boehringer Ingelheim Animal Health, Binger Str. 128, 55216 Ingelheim am Rhein, Germany
| | - Alessandro Brigo
- Roche Pharmaceutical Research & Early Development, Roche Innovation Center Basel, Grenzacherstrasse 124, 4070, Basel, Switzerland
| | | | | | - Amy Devlin
- FDA Center for Drug Evaluation and Research, Silver Spring, MD 20993, USA
| | | | - Trevor Fish
- Nelson Laboratories, Salt Lake City, Utah, USA
| | | | | | | | - David Jones
- MHRA, 10 South Colonnade, Canary Wharf, London E14 4PU
| | - Agnes Karmaus
- Integrated Laboratory Systems, LLC, Morrisville, NC, USA
| | | | - Elena Lo Piparo
- Chemical Food Safety Group, Nestlé Research, Lausanne, Switzerland
| | - Federica Madia
- European Commission, Joint Research Centre (JRC), Ispra, Italy
| | | | | | | | - Jordi Mestre
- IMIM Institut Hospital Del Mar d’Investigacions Mèdiques and Universitat Pompeu Fabra, Doctor Aiguader 88, Parc de Recerca Biomèdica, 08003 Barcelona, Spain
- Chemotargets SL, Baldiri Reixac 4, Parc Científic de Barcelona, 08028 Barcelona, Spain
| | | | | | - Moiz Mumtaz
- Office of the Associate Director for Science, Agency for Toxic Substances and Disease Registry, Centers for Disease Control and Prevention, Atlanta, GA 30333, USA
| | - Wolfgang Muster
- Roche Pharmaceutical Research & Early Development, Roche Innovation Center Basel, Grenzacherstrasse 124, 4070, Basel, Switzerland
| | | | - Grace Patlewicz
- Centre for Computational Toxicology and Exposure (CCTE), US Environmental Protection Agency, Research Triangle Park, NC, USA
| | | | - Alessandra Roncaglioni
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - Patricia Ruiz
- Centers for Disease Control and Prevention (CDC), Atlanta, GA 30341, USA
| | - Diana Suarez
- FSTox Consulting LTD, 2 Brooks Road Raunds Wellingborough NN9 6NS
| | | | - Jean-Pierre Valentin
- UCB-Biopharma SRL, Development Science, Avenue de l’industrie, Braine l’Alleud, Wallonia, Belgium
| | - Ioanna Vardakou
- British American Tobacco (Investments) Ltd., R&D Centre, Southampton, Hampshire SO15 8TL, UK
| | | | - Glenn Myatt
- Instem, 1393 Dublin Rd, Columbus, OH 43215, USA
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Corrigendum to a cross-industry collaboration to assess if acute toxicity (Q)SAR models are fit-for-purpose for GHS classification and labelling. Regulatory toxicology and pharmacology (2021) 104843. Regul Toxicol Pharmacol 2022; 131:105165. [PMID: 35474000 PMCID: PMC9200224 DOI: 10.1016/j.yrtph.2022.105165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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7
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Karmaus AL, Mansouri K, To KT, Blake B, Fitzpatrick J, Strickland J, Patlewicz G, Allen D, Casey W, Kleinstreuer N. Evaluation of Variability across Rat Acute Oral Systemic Toxicity Studies. Toxicol Sci 2022; 188:34-47. [PMID: 35426934 PMCID: PMC9237992 DOI: 10.1093/toxsci/kfac042] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Regulatory agencies rely upon rodent in vivo acute oral toxicity data to determine hazard categorization, require appropriate precautionary labeling, and perform quantitative risk assessments. As the field of toxicology moves toward animal-free new approach methodologies (NAMs), there is a pressing need to develop a reliable, robust reference data set to characterize the reproducibility and inherent variability in the in vivo acute oral toxicity test method, which would serve to contextualize results and set expectations regarding NAM performance. Such a data set is also needed for training and evaluating computational models. To meet these needs, rat acute oral LD50 data from multiple databases were compiled, curated, and analyzed to characterize variability and reproducibility of results across a set of up to 2441 chemicals with multiple independent study records. Conditional probability analyses reveal that replicate studies only result in the same hazard categorization on average at 60% likelihood. Although we did not have sufficient study metadata to evaluate the impact of specific protocol components (eg, strain, age, or sex of rat, feed used, treatment vehicle, etc.), studies were assumed to follow standard test guidelines. We investigated, but could not attribute, various chemical properties as the sources of variability (ie, chemical structure, physiochemical properties, functional use). Thus, we conclude that inherent biological or protocol variability likely underlies the variance in the results. Based on the observed variability, we were able to quantify a margin of uncertainty of ±0.24 log10 (mg/kg) associated with discrete in vivo rat acute oral LD50 values.
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Affiliation(s)
- Agnes L Karmaus
- Integrated Laboratory Systems, LLC, Morrisville, NC, 27560, USA
| | - Kamel Mansouri
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, National Institute of Environmental Health Sciences, Research Triangle Park, NC, 27709, USA
| | - Kimberly T To
- Integrated Laboratory Systems, LLC, Morrisville, NC, 27560, USA
| | - Bevin Blake
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, National Institute of Environmental Health Sciences, Research Triangle Park, NC, 27709, USA
| | - Jeremy Fitzpatrick
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, 27711, USA
| | - Judy Strickland
- Integrated Laboratory Systems, LLC, Morrisville, NC, 27560, USA
| | - Grace Patlewicz
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, 27711, USA
| | - David Allen
- Integrated Laboratory Systems, LLC, Morrisville, NC, 27560, USA
| | - Warren Casey
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, National Institute of Environmental Health Sciences, Research Triangle Park, NC, 27709, USA
| | - Nicole Kleinstreuer
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, National Institute of Environmental Health Sciences, Research Triangle Park, NC, 27709, USA
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Abstract
In this chapter, we give a brief overview of the regulatory requirements for acute systemic toxicity information in the European Union, and we review structure-based computational models that are available and potentially useful in the assessment of acute systemic toxicity. Emphasis is placed on quantitative structure-activity relationship (QSAR) models implemented by means of a range of software tools. The most recently published literature models for acute systemic toxicity are also discussed, and perspectives for future developments in this field are offered.
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Affiliation(s)
- Ivanka Tsakovska
- Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Sofia, Bulgaria.
| | - Antonia Diukendjieva
- Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Sofia, Bulgaria
| | - Andrew P Worth
- European Commission, Joint Research Centre (JRC), Ispra, Italy
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